Abstract: The present invention includes compositions and methods for detecting the presence of: a cytomegalovirus or an immune response relevant to a cytomegalovirus infection including T cells responsive to one or more cytomegalovirus peptides or proteins comprising, consisting of, or consisting essentially of: one or more amino acid sequences selected from those sequences set forth in Table 1 or Table 2, or a subsequence, portion, homologue, variant or derivative thereof; a fusion protein comprising one or more amino acid sequences selected from those sequences set forth in Table 1 or Table 2; a pool of 2 or more peptides selected from the amino acid sequences set forth in Table 1 or Table 2; or a polynucleotide that encodes one or more peptides or proteins, comprising, consisting of, or consisting essentially of an amino acid sequence selected from those sequences set forth in Table 1 or Table 2, or a subsequence, portion, homologue, variant or derivative thereof.

Abstract: Provided herein, inter alia, are compositions and methods for treating or preventing viral infections. The methods and compositions may modulate entry of a virus into a cell, viral fusion to a cell, or cell to cell spread of the virus by targeting one or more viral proteins or ligands thereof. The methods and compositions provided herein including embodiments thereof are contemplated to be especially effective for treating or preventing cytomegalovirus infections.

Abstract: Data formatting rules to convert data from one form to another form are automatically determined based on a user's edits. A machine learning heuristic is applied to a user's edits to determine a data formatting rule that may be applied to data. For example, a user may make edits that add/remove characters from data, concatenate data, extract data, rename data, and the like. The machine learning heuristic may be automatically triggered in response to an event (e.g. after a predetermined number of edits are made to a same type of data) or manually triggered (e.g. selecting a user interface option). The data formatting rule may be applied to other data and the results of the formatting reviewable by the user. Based on further edits/reviews, the data formatting rule may be updated. The data formatting rules may be stored for later use.

Abstract: A composition comprising a TRAIL-R2 receptor or fragment thereof bound to a ligand in crystalline form is presently provided along with novel binding sites and binding agents of a TRAIL receptor. Also provided are methods of designing a compound, protein or peptide and identifying a binding agent that interacts with a TRAIL receptor. The present invention further provides methods of modulating binding of a TRAIL receptor to a ligand, the methods comprising contacting the TRAIL receptor with a binding agent, ligand, or an agonist or antagonist thereof, that interacts with a novel binding site described herein.

Abstract: A composition comprising a TRAIL-R2 receptor or fragment thereof bound to a ligand in crystalline form is presently provided along with novel binding sites and binding agents of a TRAIL receptor. Also provided are methods of designing a compound, protein or peptide and identifying a binding agent that interacts with a TRAIL receptor. The present invention further provides methods of modulating binding of a TRAIL receptor to a ligand, the methods comprising contacting the TRAIL receptor with a binding agent, ligand, or an agonist or antagonist thereof, that interacts with a novel binding site described herein.

Abstract: Animation and visuals are displayed when automatically filling/changing values within a document. A scan animation may be displayed to assist a user in understanding that an action is about to occur using their recent edit(s). A fill animation may be displayed while the suggested values are automatically filled within the document. For example, the suggested values may be displayed according to an uncommitted state (e.g. light grey text) as compared to a committed state (e.g. black text) of related values. The suggested values may be accepted/rejected while they are displayed according to the uncommitted state. When the suggested values are rejected, the suggested values are removed from the display. When the suggested values are accepted, the suggested values are transitioned to the committed state. When a user performs an edit on one or more of the suggested values, a change animation may be displayed.

Abstract: Data formatting rules to convert data from one form to another form are automatically determined based on a user's edits. A machine learning heuristic is applied to a user's edits to determine a data formatting rule that may be applied to data. For example, a user may make edits that add/remove characters from data, concatenate data, extract data, rename data, and the like. The machine learning heuristic may be automatically triggered in response to an event (e.g. after a predetermined number of edits are made to a same type of data) or manually triggered (e.g. selecting a user interface option). The data formatting rule may be applied to other data and the results of the formatting reviewable by the user. Based on further edits/reviews, the data formatting rule may be updated. The data formatting rules may be stored for later use.